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- Tasks: Conduct cutting-edge research in a dynamic trading environment across various asset classes.
- Company: Join a leading firm with a collaborative and transparent research culture.
- Benefits: Competitive salary, access to top-tier data infrastructure, and opportunities for professional growth.
- Other info: Work in a vibrant environment with exposure to major financial hubs globally.
- Why this job: Make a real impact by developing innovative trading models and strategies.
- Qualifications: 3-6 years in systematic trading, strong statistical skills, and Python proficiency.
The predicted salary is between 60000 - 80000 £ per year.
This is a systematic quant researcher role within a cross-asset trading environment spanning holding periods from hours to weeks. You will own the full research pipeline: signal generation, testing, portfolio-level analysis, and work with live capital. The asset universe is broad: equities, futures, FX, credit, commodities, and ETF structures all feature. The problems are genuinely hard: signal decay, regime sensitivity, execution friction, and cross-asset correlation structure all matter here. You will be expected to form views, test them rigorously, and defend them.
You will have access to data and compute infrastructure at a scale very few firms can match. Research custom trading models to compete with the scale of frontier LLMs, consuming trillions of tokens of market data. Experimentation here is not constrained by tooling, it is constrained by the quality of your ideas.
Research here is a shared endeavour, not a collection of siloed books. Every researcher has full visibility into every active strategy's code. There are no black boxes, no protected territories. The expectation is that collective understanding produces better research than individual ownership. Strategies are sized for their contribution to the portfolio as a whole, not as standalone entities. That means your work is evaluated at the system level, which rewards researchers who think carefully about covariance, capacity, and cross-strategy interaction, not just isolated backtest Sharpe.
You have 3-6 years of experience in a systematic trading environment, a hedge fund, prop trading firm, or closely related research role. You have built and shipped predictive models against real market data, not just in simulation.
- Core requirements:
- Strong statistical foundations: time-series analysis, factor modelling, signal research
- Python proficiency; C++ experience strongly preferred (you will be interacting with C++ day to day)
- Experience across more than one asset class, or a clear track record in one with genuine appetite to work cross-asset
- Ability to take a research idea from hypothesis to backtested strategy to production-ready code
- Comfort operating in an environment where your work is visible and subject to peer scrutiny
The right mindset: You are intellectually honest about what your models do and don’t explain. You are more interested in understanding market structure than in protecting alpha. You find the idea of a shared codebase appealing rather than threatening.
London is a focus area, but realistically anywhere across the major financial hubs (NYC, Singapore, Hong Kong, Chicago).
Quantitative Researcher in City of London employer: Augmentti
Contact Detail:
Augmentti Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative Researcher in City of London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect on LinkedIn. The more people you know, the better your chances of landing that dream role as a Quantitative Researcher.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your predictive models and research projects. This is your chance to demonstrate your Python and C++ prowess, so make it shine!
✨Tip Number 3
Prepare for those interviews! Brush up on your statistical foundations and be ready to discuss your thought process behind your models. Remember, they want to see how you think, not just what you know.
✨Tip Number 4
Apply through our website! We love seeing candidates who are genuinely interested in joining our team. Plus, it gives you a direct line to us, making it easier to stand out from the crowd.
We think you need these skills to ace Quantitative Researcher in City of London
Some tips for your application 🫡
Show Your Research Skills: When you're writing your application, make sure to highlight your experience in systematic trading and research. We want to see how you've tackled complex problems like signal decay and execution friction, so don't hold back on the details!
Be Clear About Your Technical Skills: We love a good Python whiz, but if you've got C++ skills too, that's a bonus! Make sure to mention any relevant programming languages and tools you've used in your previous roles, especially those that relate to building predictive models.
Emphasise Collaboration: Since we operate as a team, it's important to show that you value collaboration over individual ownership. Share examples of how you've worked with others to improve research outcomes or how you've contributed to a shared codebase.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen to join our team at StudySmarter!
How to prepare for a job interview at Augmentti
✨Know Your Models Inside Out
Make sure you can explain your predictive models in detail. Be ready to discuss how you've built and tested them against real market data, not just simulations. This shows that you understand the practical implications of your work.
✨Embrace Collaboration
Since the team values shared research, be prepared to discuss how you've worked with others in the past. Highlight experiences where collective understanding led to better outcomes, and express your enthusiasm for a transparent codebase.
✨Demonstrate Statistical Savvy
Brush up on your statistical foundations, especially time-series analysis and factor modelling. Be ready to tackle questions that test your knowledge in these areas, as they are crucial for the role.
✨Show Your Cross-Asset Knowledge
If you have experience across multiple asset classes, make sure to highlight it. If you're more specialised, convey your genuine interest in expanding your expertise. This will show your adaptability and willingness to learn.